APSTA-GE 2003: Intermediate Quantitative Methods

class: center, middle, inverse, title-slide

# APSTA-GE 2003: Intermediate Quantitative Methods
## Lab Section 003, Week 10
### New York University
### 11/10/2020

---

## Reminders
  
- Sample Assignment Solutions Available
  - In [this Google Drive folder](https://drive.google.com/drive/folders/1b3fHkxC81ov2Sw8uPomXpXHyar2w2eQu?usp=sharing)

- No Assignment Due
 - <(￣︶￣)>
- Office hours
 - Monday 9:00 - 10:00am (EST)
 - Wednesday 12:30 - 1:30pm (EST)
 - Additional time slots available
 - Sign-up sheet [HERE](https://docs.google.com/spreadsheets/d/1YY38yj8uCNIm1E7jaI9TJC494Pye2-Blq9eSK_eh6tI/edit?usp=sharing)
 
- Office hour Zoom link [HERE](https://nyu.zoom.us/j/9985119253)
 
- Office hour notes
 - Available on NYU Classes

---

## Today's Topics

- Analyzing NYC Property Sales Data using Multiple Regression Modeling

---

## Introduction

In this lab, we will explore a property market dataset using multiple regression modeling. We will examine how the sale price varied across a 12-month period in 2016 - 2017. Specifically, we will try to understand the covariates that affect the sale price. Our research questions are:

1. How did the sale price of NYC properties vary in 2016 - 2017?

2. What contributed to the increase of the sale price?

3. How do we predict the sale price based on the columns we have?

## Design

We will conduct an Exploratory Data Analysis (EDA), followed by a multiple regression analysis, to try to answer these questions.

---

## Dataset

The dataset records every building or building unit (apartment, etc.) sold in the New York City property market over a 12-month period in 2016 - 2017. (Source: [Kaggle](https://www.kaggle.com/new-york-city/nyc-property-sales))

The dataset contains the location, address, type, sale price, and sale data of building units sold.

[Dataset file here](https://drive.google.com/file/d/1JjV_UZW6eCeAuqyTY3yV5W3WZdZ9ve2A/view?usp=sharing)

First, load the data into R:

```r
dat <- read.csv("nyc-rolling-sales.csv", header = TRUE, 
* na.strings = c("NA", " - "))
dim(dat)
```

```
## [1] 84548    22
```

Number of observations: 84,548

Number of columns: 22

Column Names:

```r
colnames(dat)
```

```
##  [1] "X"                              "BOROUGH"                       
##  [3] "NEIGHBORHOOD"                   "BUILDING.CLASS.CATEGORY"       
##  [5] "TAX.CLASS.AT.PRESENT"           "BLOCK"                         
##  [7] "LOT"                            "EASE.MENT"                     
##  [9] "BUILDING.CLASS.AT.PRESENT"      "ADDRESS"                       
## [11] "APARTMENT.NUMBER"               "ZIP.CODE"                      
## [13] "RESIDENTIAL.UNITS"              "COMMERCIAL.UNITS"              
## [15] "TOTAL.UNITS"                    "LAND.SQUARE.FEET"              
## [17] "GROSS.SQUARE.FEET"              "YEAR.BUILT"                    
## [19] "TAX.CLASS.AT.TIME.OF.SALE"      "BUILDING.CLASS.AT.TIME.OF.SALE"
## [21] "SALE.PRICE"                     "SALE.DATE"
```

---

## Codebook - Part 1

(Source: [Glossary of Terms for Property Sales Files - NYC](https://www1.nyc.gov/assets/finance/downloads/pdf/07pdf/glossary_rsf071607.pdf))

- X: integer: index
- BOROUGH: integer: borough name
  - 1: Manhattan
  - 2: Bronx
  - 3: Brooklyn
  - 4: Queens
  - 5: Staten Island
- NEIGHBORHOOD: character: neighborhood name
- BUILDING.CLASS.CATEGORY: category: identify similar properties by broad usage (e.g. One Family Homes) without looking up individual Building Classes.
- TAX.CLASS.AT.PRESENT: category: tax classes
  - Class 1: residential property of up to three units
  - Class 2: all other properties that are primarily residential
  - Class 3: property owned by a gas, telephone or electric company
  - Class 4: all other commercial properties
- BLOCK: integer: sub-division of the borough
- LOT: integer: a tax lot
- EASE.MENT: logical: easement or not
- BUILDING.CLASS.AT.PRESENT: category: constructive use
- ADDRESS: character: address
- APARTMENT.NUMBER: character: apt. number
- ZIP.CODE: category: zip code
- RESIDENTIAL.UNITS: integer: number of residential units
- COMMERCIAL.UNITS: integer: number of commercial units
- TOTAL.UNITs: integer: total number of units
- LAND.SQUARE.FEET: character: land sqft
- GROSS.SQUARE.FEET: character: gross sqft
- YEAR.BUILT: integer: year built
- BUILDING.CLASS.AT.TIME.OF.SALE: category: constructive use
- SALE.PRICE: character: sale price
- SALE.DATE: character: sale date

---

## Pre-process

First, make column names to lowercase using the `stringr` package. Then, replace `.` with `_`.

```r
*colnames(dat) <- str_to_lower(colnames(dat))
*colnames(dat) <- str_replace_all(colnames(dat), pattern = "[.]", replacement = "_")
colnames(dat)
```

```
##  [1] "x"                              "borough"                       
##  [3] "neighborhood"                   "building_class_category"       
##  [5] "tax_class_at_present"           "block"                         
##  [7] "lot"                            "ease_ment"                     
##  [9] "building_class_at_present"      "address"                       
## [11] "apartment_number"               "zip_code"                      
## [13] "residential_units"              "commercial_units"              
## [15] "total_units"                    "land_square_feet"              
## [17] "gross_square_feet"              "year_built"                    
## [19] "tax_class_at_time_of_sale"      "building_class_at_time_of_sale"
## [21] "sale_price"                     "sale_date"
```

---

## Pre-process

- Examine if there is any missing value.

```r
n_col <- ncol(dat)
paste("Column Index", "Number of NAs", sep = ", ")
```

```
## [1] "Column Index, Number of NAs"
```

```r
for (index in 1:n_col) {
  print(paste(index, ", ", colnames(dat)[index], ", ", sum(is.na(dat[, index])), sep = ""))
}
```

```
## [1] "1, x, 0"
## [1] "2, borough, 0"
## [1] "3, neighborhood, 0"
## [1] "4, building_class_category, 0"
## [1] "5, tax_class_at_present, 0"
## [1] "6, block, 0"
## [1] "7, lot, 0"
## [1] "8, ease_ment, 84548"
## [1] "9, building_class_at_present, 0"
## [1] "10, address, 0"
## [1] "11, apartment_number, 0"
## [1] "12, zip_code, 0"
## [1] "13, residential_units, 0"
## [1] "14, commercial_units, 0"
## [1] "15, total_units, 0"
## [1] "16, land_square_feet, 26252"
## [1] "17, gross_square_feet, 27612"
## [1] "18, year_built, 0"
## [1] "19, tax_class_at_time_of_sale, 0"
## [1] "20, building_class_at_time_of_sale, 0"
## [1] "21, sale_price, 14561"
## [1] "22, sale_date, 0"
```

---

## Trim the dataset

Drop unrelated columns.

```r
col_name_to_drop <- c("x", "ease_ment", "neighborhood", 
 "building_class_at_present", "tax_class_at_present", 
 "address", "apartment_number", "tax_class_at_time_of_sale")
col_index_to_drop <- which(colnames(dat) %in% col_name_to_drop)
dat <- dat[, -col_index_to_drop]
dim(dat)
```

```
## [1] 84548    14
```

Drop rows with missing values.

```r
col_name_w_NA <- c("sale_price", "land_square_feet", "gross_square_feet")
col_index_w_NA <- which(colnames(dat) %in% col_name_w_NA)
row_w_NA <- which(is.na(dat[, col_index_w_NA[1]]))
dat <- dat[-row_w_NA, ]
row_w_NA <- which(is.na(dat[, col_index_w_NA[2]]))
dat <- dat[-row_w_NA, ]
row_w_NA <- which(is.na(dat[, col_index_w_NA[3]]))
dat <- dat[-row_w_NA, ]
dim(dat)
```

```
## [1] 48244    14
```

```r
table(is.na(dat))
```

```
## 
##  FALSE 
## 675416
```

---

## Descriptive Analysis

```r
colnames(dat)
```

```
##  [1] "borough"                        "building_class_category"       
##  [3] "block"                          "lot"                           
##  [5] "zip_code"                       "residential_units"             
##  [7] "commercial_units"               "total_units"                   
##  [9] "land_square_feet"               "gross_square_feet"             
## [11] "year_built"                     "building_class_at_time_of_sale"
## [13] "sale_price"                     "sale_date"
```

```r
dat$sale_date <- as.Date(dat$sale_date)
```

---

## Borough

```r
p <- ggplot(dat, aes(x = factor(borough))) + 
 geom_histogram(stat="count") + 
 scale_x_discrete(labels = c("Man", "Bronx", "Brooklyn", "Queens", "SI")) + 
 theme_minimal()
ggplotly(p)
```

<div id="htmlwidget-05e17007df41ba87baec" style="width:720px;height:432px;" class="plotly html-widget"></div>
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---

## Average Sale Price Per Borough

```r
average_sales_per_borough <- dat %>% 
 group_by(borough) %>%
 summarize(total = n(), average_sales = mean(sale_price))
average_sales_per_borough
```

```
## # A tibble: 5 x 3
## borough total average_sales
## <int> <int> <dbl>
## 1 1 1006 18517220.
## 2 2 7049 590194.
## 3 3 24047 834488.
## 4 4 11078 912305.
## 5 5 5064 528604.
```

---

## Viz

```r
p <- ggplot(average_sales_per_borough, aes(x = borough, y = average_sales)) +
 geom_point() + 
 scale_x_discrete(labels = c("Man", "Bronx", "Brooklyn", "Queens", "SI")) + 
 theme_minimal()
ggplotly(p)
```

<div id="htmlwidget-8506aa8ec1d5ce8c4d98" style="width:720px;height:432px;" class="plotly html-widget"></div>
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---

## Building Class Category

```r
building_class_cat <- dat %>%
 group_by(borough, building_class_category) %>%
 summarize(total = n()) %>% 
 arrange(desc(total), borough)
building_class_cat
```

```
## # A tibble: 146 x 3
## # Groups: borough [5]
## borough building_class_category total
## <int> <chr> <int>
## 1 3 "02 TWO FAMILY DWELLINGS " 6040
## 2 4 "01 ONE FAMILY DWELLINGS " 5927
## 3 4 "02 TWO FAMILY DWELLINGS " 3683
## 4 5 "01 ONE FAMILY DWELLINGS " 3529
## 5 3 "01 ONE FAMILY DWELLINGS " 3497
## 6 3 "13 CONDOS - ELEVATOR APARTMENTS " 2817
## 7 3 "03 THREE FAMILY DWELLINGS " 2175
## 8 3 "10 COOPS - ELEVATOR APARTMENTS " 2158
## 9 2 "02 TWO FAMILY DWELLINGS " 1909
## 10 3 "07 RENTALS - WALKUP APARTMENTS " 1605
## # … with 136 more rows
```

---

## Viz

```r
p <- ggplot(building_class_cat, aes(x = building_class_category, y = total)) +
 geom_point() + 
 theme_classic()
ggplotly(p)
```

<div id="htmlwidget-2a5e092b3b865ac23d51" style="width:720px;height:432px;" class="plotly html-widget"></div>
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---

## Zip code

```r
zip_code <- dat %>%
 group_by(zip_code) %>%
 summarize(average_sales = mean(sale_price)) %>%
 arrange(desc(average_sales))
zip_code
```

```
## # A tibble: 180 x 2
## zip_code average_sales
## <int> <dbl>
## 1 10167 2210000000 
## 2 10004 376053600 
## 3 10005 311668843 
## 4 10006 203345000 
## 5 10001 90572732.
## 6 10038 34163229.
## 7 10018 33632200 
## 8 10010 33538515.
## 9 10016 33370155.
## 10 10017 29581143.
## # … with 170 more rows
```

---

## Viz

```r
p <- ggplot(zip_code, aes(x = factor(zip_code), y = average_sales)) +
 geom_point() + 
 theme_classic()
ggplotly(p)
```

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---

## Other Columns

```r
summary(dat$land_square_feet)
```

```
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##       0    1413    2140    3358    3071 4228300
```

```r
summary(dat$year_built)
```

```
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##       0    1920    1931    1828    1961    2017
```

```r
summary(dat$total_units)
```

```
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##    0.000    1.000    1.000    2.835    2.000 2261.000
```

---

## Check correlation

```r
cor_dat <- cor(dat[, -c(2, 12, 14)])
*corrplot::corrplot(cor_dat, type = "lower")
```

![](data:image/png;base64,#Lab10-Slides-111020_files/figure-html/actual-corr-1.png)

---

## Let's Build Our Model Together!

## Contact

Tong Jin

- Email: tj1061@nyu.edu
- Office Hours
  - Mondays, 9 - 10am (EST)
  - Wednesdays, 12:30 - 1:30pm (EST)